fix to bug in gpu shared variable constructor when broadcastable flags specified by non-tuple

theano/sandbox/cuda/tests/test_var.py

+import numpy
+
+from theano.sandbox.cuda.var import float32_shared_constructor as f32sc
+from theano.sandbox.cuda import CudaNdarrayType
+
+def test_float32_shared_constructor():
+
+    npy_row = numpy.zeros((1,10), dtype='float32')
+
+    def eq(a,b):
+        return a==b
+
+    # test that we can create a CudaNdarray
+    assert (f32sc(npy_row).type == CudaNdarrayType((False, False)))
+
+    # test that broadcastable arg is accepted, and that they
+    # don't strictly have to be tuples
+    assert eq(
+            f32sc(npy_row, broadcastable=(True, False)).type,
+            CudaNdarrayType((True, False)))
+    assert eq(
+            f32sc(npy_row, broadcastable=[True, False]).type,
+            CudaNdarrayType((True, False)))
+    assert eq(
+            f32sc(npy_row, broadcastable=numpy.array([True, False])).type,
+            CudaNdarrayType([True, False]))
+
+    # test that we can make non-matrix shared vars
+    assert eq(
+            f32sc(numpy.zeros((2,3,4,5), dtype='float32')).type,
+            CudaNdarrayType((False,)*4))

theano/sandbox/cuda/var.py

@@ -195,7 +195,9 @@ def float32_shared_constructor(value, name=None, strict=False,
         else:
             deviceval = value.copy()
     else:
-        deviceval = type_support_filter(value, broadcastable, False, None)
+        # type.broadcastable is guaranteed to be a tuple, which this next
+        # function requires
+        deviceval = type_support_filter(value, type.broadcastable, False, None)
 
     try:
         rval = CudaNdarraySharedVariable(type=type, value=deviceval, name=name, strict=strict)